Predictive cloud provisioning based on human behaviors and heuristics

ABSTRACT

A system for predictively provisioning cloud resources based on human behaviors and heuristics includes a computer processor and logic executable by the computer processor. The logic is configured to implement a method. The method includes monitoring a collection of events relating to a customer application as well as monitoring an infrastructure load on resources for the customer application. A causal relationship is evaluated between an event and the infrastructure load. A predictive rule is then constructed based on the causal relationship. Resource requirements are anticipated based on the predictive rule and a provisioning of resources in a service domain is requested for the anticipated resource requirements, according to exemplary embodiments.

BACKGROUND

The present invention relates generally to cloud computing, and morespecifically, to predictively provisioning cloud resources based onhuman behaviors and heuristics.

Cloud computing has introduced the ability to deploy specific workloadsto loosely coupled compute resources on demand. These computingresources may include processors, memory, network, and storage which areprovided within service domains (i.e., clouds) that provision therequired middleware and applications in response to specific workloadrequests from service subscribers. Information technology organizationsmay typically create private clouds that provision resources forspecific applications or may subscribe to public clouds that deliverinformation technology services and resources either for a fee or forfree.

BRIEF SUMMARY

According to an embodiment of the present invention, a system forpredictively provisioning cloud resources based on human behaviors andheuristics is provided. The system includes a computer processor andlogic executable by the computer processor. The logic is configured toimplement a method. The method includes monitoring a collection ofevents relating to a customer application as well as monitoring aninfrastructure load on resources for the customer application. A causalrelationship is evaluated between an event and the infrastructure load.A predictive rule is then constructed based on the causal relationship.Resource requirements are anticipated based on the predictive rule and aprovisioning of resources in a service domain is requested for theanticipated resource requirements, according to exemplary embodiments.

Additional features and advantages are realized through the techniquesof the present invention. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention. For a better understanding of the invention with theadvantages and the features, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention;

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention;

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention; and

FIG. 4 depicts a flow diagram of a process for predictively provisioningcloud resources based on human behaviors and heuristics according to anembodiment

DETAILED DESCRIPTION

Embodiments disclosed herein are directed to predictively provisioningcloud resources based on human behaviors and heuristics. Aspects ofembodiments disclosed herein include monitoring a collection of eventsrelating to a customer application as well as monitoring aninfrastructure load on resources for the customer application. A causalrelationship is evaluated between an event and the infrastructure load.A predictive rule is then constructed based on the causal relationship.Resource requirements are anticipated based on the predictive rule and aprovisioning of resources in a service domain is requested for theanticipated resource requirements, according to exemplary embodiments.

Embodiments disclosed herein provide an extension to the contemporarycloud computing model by establishing a predictive capability forprovisioning a set of cloud service resources located in private orglobal clouds. These cloud resource services may be deployed based onpredictive analytics. The predictive analytics of embodiments includeevaluating patterns of human behaviors coupled with applying heuristicsto anticipate and provision the appropriate cloud resources to ensurecapacity to meet customer requirements.

Exemplary embodiments provide a predictive capability to provision a setof service resources located in disparate clouds based on the influenceof global heuristics and human behaviors. Analytics are used to refineand maintain these heuristics via a self-learning model and anticipateresultant human behavior based on prior actual resource usage accordingto an embodiment. Human reaction to global events may drive the need foradditional computing resources in local geographies. For example,natural disasters or war may increase the need for health and humanservices or medical computing domains.

Conversely, the launch of a new technology, such as a smart phone, maydrive social media channels or significant web traffic. According toembodiments, predictive clouds could anticipate the additional computingdemands in a service domain and communicate with other clouds toproactively provision resources. Rather than wait for a callingapplication's request for services, the predictive cloud provisioningfunctionality of embodiments may construct a model of directedrequirements and issue provisioning orders to other cloud provisioningservices that would respond to the requirements. Over time, theseconnections would evolve to be more predictive including direct andindirect connections according to embodiments. As an example, politicalunrest may drive traffic to cancel vacations. In another example, thedowngrade of a hurricane may in turn reduce the number of credit cardpurchases in a particular state as people stop stockpiling and buyingemergency supplies. Such examples may define patterns that can becaptured and added to a heuristics database for future referenceaccording to embodiments.

It is understood in advance that although this invention includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a block diagram of a cloud computing node forpredictively provisioning cloud resources based on human behaviors andheuristics of an embodiment is shown. Cloud computing node 10 is onlyone example of a suitable cloud computing node and is not intended tosuggest any limitation as to the scope of use or functionality ofembodiments described herein. Regardless, cloud computing node 10 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device, alsoreferred to as a processing device. The components of computersystem/server 12 may include, but are not limited to, one or moreprocessors or processing units 16, a system memory 28, and a bus 18 thatcouples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 may include a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system/server 12, and it includes both volatile andnon-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,digital video camera 54D, digital audio recording device 54E, and/ordigital still camera 54N may communicate. Nodes 10 may communicate withone another. They may be grouped (not shown) physically or virtually, inone or more networks, such as Private, Community, Public, or Hybridclouds as described hereinabove, or a combination thereof. This allowscloud computing environment 50 to offer infrastructure, platforms and/orsoftware as services for which a cloud consumer does not need tomaintain resources on a local computing device. It is understood thatthe types of computing devices 54A-N shown in FIG. 2 are intended to beillustrative only and that computing nodes 10 and cloud computingenvironment 50 can communicate with any type of computerized device overany type of network and/or network addressable connection (e.g., using aweb browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provides pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and resource provisioning processing 67.

According to an exemplary embodiment, as world or local events unfold,resultant human behavior will start driving the need for cloudprovisioning. Policies within a heuristic database of an embodiment maycommunicate within a local cloud and across disparate cloudsbroadcasting the need for specific provisioning via a service request.According to an embodiment, the service request may be forwarded to acloud provisioning service, which would optimize the business rules(e.g., cost parameters, performance parameters, and/or locationparameters) and dynamically provision a set of servers based on theconfiguration and programmatic requirements contained in the servicerequest. The deployed cloud service may then operate on the requests andreturn results to the calling program.

A predictive dynamic cloud provisioning service of an embodimentincludes routines that collect global and/or private information, whichare both directly related and indirectly associated with the customer'sapplication. The service of an embodiment also collects loadcharacteristics that are used to create the predictive rules and causalanalysis. As this information is collected and analyzed, the service ofan embodiment generates resource requirements that are then appliedagainst business rules to keep requests within approved guidelines andnotification profiles. According to an embodiment, the appropriateapplication programming interfaces (APIs) are then invoked to create aunique request which can be provisioned from multiple clouds.

With reference to FIG. 4, a process 400 performed by an embodiment ofthe resource provisioning processing 67 is generally shown. As shown inFIG. 4, the process 400 predictively provisions cloud resources based onhuman behaviors and heuristics according to an embodiment.

At block 410, a collection of events relating to a customer applicationis monitored according to an embodiment. The process 400 maycontinuously monitor a plurality of data feeds from public sources fromthe Internet and private sources directly from businesses. According toan embodiment, the plurality of data feeds may include a selected one ormore of news feeds, network statistics (e.g., Internet statistics),weather reports, social networking data, and company data. The collectedregional or global events are stored in a heuristic database for laterevaluation, as discussed below in block 430.

At block 420, an infrastructure load on resources is monitored for thecustomer application according to an embodiment. A collection of loadmetrics from a cloud service for the customer's application is monitoredand also stored in the heuristic database for later evaluation, asdiscussed below in block 430.

At block 430, a causal relationship between an event and theinfrastructure load is evaluated according to an embodiment. Heuristicanalysis is used to update behavioral rules based on the causal links inhuman response to a particular event. In other words, the causalrelationships between the historical data collected in blocks 410 and420 are evaluated at block 430, according to an embodiment.

These relationships update the predictive rules between human responseand infrastructure forecasting, as shown in block 440. A predictive ruleis constructed based on the causal relationship according to anembodiment. The predictive rule of an embodiment forecasts humanbehavior responses to an occurrence of the event.

At block 450, resource requirements are anticipated based on thepredictive rule according to an embodiment. Using the current iterationof data collected in the heuristic database, the predictive rule may beapplied to generate anticipated cloud resource requirements and update aforecast for cloud provisioning according to the anticipated resourcerequirements. According to an embodiment, a notification and/or approvalrequest may be transmitted to a designated user (e.g., business owner)prior to requesting the provisioning of the anticipated resourcerequirements from a cloud provisioning service.

According to an embodiment, before proactively provisioning cloudresources, static business rules may be applied to the anticipatedresource requirements. The static business rules may include, but arenot limited to, one or more of priority parameters, cost parameters, andcapacity parameters. The business rules keep the provisioning requestswithin approved business guidelines and notification profiles.

Once the business rules are applied, a provisioning of resources in aservice domain is requested for the anticipated resource requirements,as shown in block 460. The provisioning request is sent to the cloud ina structured request according to an embodiment. This request may betailored to follow open standards or custom messaging.

According to an embodiment, the cloud provisioning service thencompletes the request based on its optimization rules. The optimizationrules of an embodiment may include, but are not limited to, one or moreof cost parameters, performance parameters, and location parameters.

According to another embodiment, responsive to the additionalavailability of provisioned resource capacity in the cloud, new loadmetrics will be collected as shown in block 420 and these new loadmetric will be used to validate the predictive rule.

Technical effects and benefits include a predictive provisioning of aset of cloud service resources located in private or global clouds.These cloud resource services may be deployed based on predictiveanalytics. The predictive analytics of embodiments include evaluatingpatterns of human behaviors coupled with applying heuristics toanticipate and provision the appropriate cloud resources to ensurecapacity to meet customer requirements. Analytics are used to refine andmaintain these heuristics via a self-learning model and anticipateresultant human behavior based on prior actual resource usage accordingto an embodiment.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiments were chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

Further, as will be appreciated by one skilled in the art, aspects ofthe present disclosure may be embodied as a system, method, or computerprogram product. Accordingly, aspects of the present disclosure may takethe form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A computer system, comprising: a processor forexecuting the computer readable instructions to perform a methodcomprising: monitoring a collection of world or local events relating toa customer application, wherein monitoring comprises evaluating aplurality of data feeds comprising a selected one or more of news feeds,network statistics, weather reports, social networking data, and companydata; monitoring an infrastructure load on resources for the customerapplication; evaluating a causal relationship between a particular eventfrom the collection of world or local events and the infrastructure loadbased on historical data obtained from a heuristic database, whereinevaluating the causal relationship comprises heuristic analysis toupdate a behavioral rule based on a causal link in human responses tothe particular event; constructing a predictive rule based on the causalrelationship, wherein the predictive rule forecasts future humanresponses to a future occurrence of the particular event; anticipatingresource requirements based on the predictive rule; and requesting aprovisioning of cloud resources in a service domain for the anticipatedresource requirements.
 2. The computer system of claim 1, wherein thepredictive rule forecasts human behavior responses to an occurrence ofthe particular event.
 3. The computer system of claim 1, the methodfurther comprising applying static business rules to the anticipatedresource requirements, the static business rules comprising a selectedone or more of priority parameters, cost parameters, and capacityparameters.
 4. The computer system of claim 1, wherein the anticipatingof resource requirements further comprises notifying or requestingapproval from a designated user prior to requesting the anticipatedresource requirements.
 5. The computer system of claim 1, the methodfurther comprising provisioning the resources based on a set ofoptimization rules, the optimization rules comprising a selected one ormore of cost parameters, performance parameters, and locationparameters.
 6. The computer system of claim 1, the method furthercomprising: receiving new load metrics responsive to the provisionedresources; and validating the predictive rule based on the new loadmetrics.